The striking simplicity of averaging techniques in a posteriori error control of finite element methods as well as their amazing accuracy in many numerical examples over the last decade have made them an extremely popular tool in scientific computing. Given a discrete stress or flux $P_h$ and a post-processed approximation $A(p_h)$, the a posteriori error estimator reads $\eta_A := ||p_h - A(p_h)||$. There is not even a need for an equation to compute the estimator $\eta_A$ and hence averaging techniques are employed everywhere. The most prominent example is occasionally named after Zienkiewicz and Zhu, and also called gradient recovery but preferably called averaging technique in the literature.
The first mathematical justification of the error estimator $\eta_A$ as a computable approximation of the (unknown) error $||p - p_h||$ involved the concept of superconvergence points. For highly structured meshes and a very smooth exact solution $p$, the error $||p - A(p_h)||$ of the post-processed approximation $Ap_h$ may be (much) smaller than $||p - p_h||$ of the given $p_h$. Under the assumption that $||p - A(p_h)||$= h.o.t. is in relative terms sufficiently small, the triangle inequality immediately verifies reliability, i.e.,
                                   $|| p-p_h ||  \leq C_{rel} \eta_A + $h.o.t.,
and efficiency, i.e.,
                                    $\eta_A \leq C_{eff} || p-p_h || +$ h.o.t.,
of the averaging error estimator $\eta_A$. However, the required assumptions on the symmetry of the mesh and the smoothness of the solution essentially contradict the use of adaptive grid refining when $p$ is singular and the proper treatment of boundary conditions remains unclear.
This paper aims at an actual overview on the reliability and efficiency of averaging a posteriori error control for unstructured grids. New aspects are new proofs of the efficiency of all averaging techniques and for all problems.